Overview

Dataset statistics

Number of variables16
Number of observations99001
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 MiB
Average record size in memory128.0 B

Variable types

Numeric15
Categorical1

Alerts

df_index is highly correlated with friend_count and 1 other fieldsHigh correlation
age is highly correlated with dob_yearHigh correlation
dob_year is highly correlated with ageHigh correlation
friend_count is highly correlated with df_index and 1 other fieldsHigh correlation
friendships_initiated is highly correlated with df_index and 1 other fieldsHigh correlation
likes is highly correlated with mobile_likes and 1 other fieldsHigh correlation
likes_received is highly correlated with mobile_likes_received and 1 other fieldsHigh correlation
mobile_likes is highly correlated with likesHigh correlation
mobile_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
www_likes is highly correlated with likesHigh correlation
www_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
likes_received is highly skewed (γ1 = 112.0734525) Skewed
mobile_likes_received is highly skewed (γ1 = 107.530233) Skewed
www_likes_received is highly skewed (γ1 = 126.2560557) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
userid has unique values Unique
friend_count has 1962 (2.0%) zeros Zeros
friendships_initiated has 2997 (3.0%) zeros Zeros
likes has 22308 (22.5%) zeros Zeros
likes_received has 24428 (24.7%) zeros Zeros
mobile_likes has 35055 (35.4%) zeros Zeros
mobile_likes_received has 30003 (30.3%) zeros Zeros
www_likes has 60999 (61.6%) zeros Zeros
www_likes_received has 36864 (37.2%) zeros Zeros

Reproduction

Analysis started2022-10-30 12:40:24.097539
Analysis finished2022-10-30 12:40:56.778247
Duration32.68 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct99001
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49500.99966
Minimum0
Maximum99002
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:56.874340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4950
Q124750
median49501
Q374252
95-th percentile94052
Maximum99002
Range99002
Interquartile range (IQR)49502

Descriptive statistics

Standard deviation28580.06346
Coefficient of variation (CV)0.5773633595
Kurtosis-1.200019068
Mean49500.99966
Median Absolute Deviation (MAD)24751
Skewness2.682051442 × 10-8
Sum4900648467
Variance816820027.5
MonotonicityStrictly increasing
2022-10-30T18:10:56.965055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
660341
 
< 0.1%
660081
 
< 0.1%
660071
 
< 0.1%
660061
 
< 0.1%
660051
 
< 0.1%
660041
 
< 0.1%
660031
 
< 0.1%
660021
 
< 0.1%
660011
 
< 0.1%
Other values (98991)98991
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
990021
< 0.1%
990011
< 0.1%
990001
< 0.1%
989991
< 0.1%
989981
< 0.1%
989971
< 0.1%
989961
< 0.1%
989951
< 0.1%
989941
< 0.1%
989931
< 0.1%

userid
Real number (ℝ≥0)

UNIQUE

Distinct99001
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1597042.018
Minimum1000008
Maximum2193542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:57.058769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1000008
5-th percentile1060617
Q11298804
median1596148
Q31895741
95-th percentile2133359
Maximum2193542
Range1193534
Interquartile range (IQR)596937

Descriptive statistics

Standard deviation344058.5093
Coefficient of variation (CV)0.2154348511
Kurtosis-1.199551893
Mean1597042.018
Median Absolute Deviation (MAD)298438
Skewness0.0001062213482
Sum1.581087568 × 1011
Variance1.183762578 × 1011
MonotonicityNot monotonic
2022-10-30T18:10:57.164245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20943821
 
< 0.1%
20514171
 
< 0.1%
15292031
 
< 0.1%
20957621
 
< 0.1%
19103221
 
< 0.1%
17876991
 
< 0.1%
11911011
 
< 0.1%
15693261
 
< 0.1%
10770051
 
< 0.1%
19354121
 
< 0.1%
Other values (98991)98991
> 99.9%
ValueCountFrequency (%)
10000081
< 0.1%
10000131
< 0.1%
10000151
< 0.1%
10000381
< 0.1%
10000591
< 0.1%
10000611
< 0.1%
10000681
< 0.1%
10000941
< 0.1%
10001031
< 0.1%
10001251
< 0.1%
ValueCountFrequency (%)
21935421
< 0.1%
21935381
< 0.1%
21935221
< 0.1%
21934991
< 0.1%
21934851
< 0.1%
21934731
< 0.1%
21934681
< 0.1%
21934651
< 0.1%
21934601
< 0.1%
21934181
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.27904769
Minimum13
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:57.274775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q120
median28
Q350
95-th percentile90
Maximum113
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.58843564
Coefficient of variation (CV)0.6059284515
Kurtosis1.561913546
Mean37.27904769
Median Absolute Deviation (MAD)10
Skewness1.415329082
Sum3690663
Variance510.2374245
MonotonicityNot monotonic
2022-10-30T18:10:57.362300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185196
 
5.2%
234404
 
4.4%
194391
 
4.4%
203769
 
3.8%
213671
 
3.7%
253641
 
3.7%
173283
 
3.3%
163086
 
3.1%
223032
 
3.1%
242827
 
2.9%
Other values (91)61701
62.3%
ValueCountFrequency (%)
13484
 
0.5%
141925
 
1.9%
152618
2.6%
163086
3.1%
173283
3.3%
185196
5.2%
194391
4.4%
203769
3.8%
213671
3.7%
223032
3.1%
ValueCountFrequency (%)
113202
 
0.2%
11218
 
< 0.1%
11118
 
< 0.1%
11015
 
< 0.1%
1099
 
< 0.1%
1081661
1.7%
10798
 
0.1%
106125
 
0.1%
10580
 
0.1%
10473
 
0.1%

dob_day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5305502
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:57.473327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median14
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.015594711
Coefficient of variation (CV)0.6204579034
Kurtosis-1.188972204
Mean14.5305502
Median Absolute Deviation (MAD)8
Skewness0.1078302388
Sum1438539
Variance81.28094799
MonotonicityNot monotonic
2022-10-30T18:10:57.553846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17899
 
8.0%
104030
 
4.1%
153555
 
3.6%
53545
 
3.6%
123413
 
3.4%
23409
 
3.4%
33291
 
3.3%
173266
 
3.3%
203263
 
3.3%
143218
 
3.3%
Other values (21)60112
60.7%
ValueCountFrequency (%)
17899
8.0%
23409
3.4%
33291
3.3%
43217
3.2%
53545
3.6%
63108
 
3.1%
73010
 
3.0%
83202
3.2%
93003
 
3.0%
104030
4.1%
ValueCountFrequency (%)
311507
1.5%
302530
2.6%
292508
2.5%
282955
3.0%
272755
2.8%
262753
2.8%
253217
3.2%
242807
2.8%
232864
2.9%
222838
2.9%

dob_year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1975.720952
Minimum1900
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:57.643765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1923
Q11963
median1985
Q31993
95-th percentile1998
Maximum2000
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.58843564
Coefficient of variation (CV)0.01143300911
Kurtosis1.561913546
Mean1975.720952
Median Absolute Deviation (MAD)10
Skewness-1.415329082
Sum195598350
Variance510.2374245
MonotonicityNot monotonic
2022-10-30T18:10:57.742375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19955196
 
5.2%
19904404
 
4.4%
19944391
 
4.4%
19933769
 
3.8%
19923671
 
3.7%
19883641
 
3.7%
19963283
 
3.3%
19973086
 
3.1%
19913032
 
3.1%
19892827
 
2.9%
Other values (91)61701
62.3%
ValueCountFrequency (%)
1900202
 
0.2%
190118
 
< 0.1%
190218
 
< 0.1%
190315
 
< 0.1%
19049
 
< 0.1%
19051661
1.7%
190698
 
0.1%
1907125
 
0.1%
190880
 
0.1%
190973
 
0.1%
ValueCountFrequency (%)
2000484
 
0.5%
19991925
 
1.9%
19982618
2.6%
19973086
3.1%
19963283
3.3%
19955196
5.2%
19944391
4.4%
19933769
3.8%
19923671
3.7%
19913032
3.1%

dob_month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.283360774
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:57.832383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.52962052
Coefficient of variation (CV)0.5617408655
Kurtosis-1.240380283
Mean6.283360774
Median Absolute Deviation (MAD)3
Skewness0.03129218052
Sum622059
Variance12.45822101
MonotonicityNot monotonic
2022-10-30T18:10:57.922925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111771
11.9%
108476
8.6%
58271
8.4%
88266
8.3%
38110
8.2%
78021
8.1%
97939
8.0%
127893
8.0%
47810
7.9%
27632
7.7%
Other values (2)14812
15.0%
ValueCountFrequency (%)
111771
11.9%
27632
7.7%
38110
8.2%
47810
7.9%
58271
8.4%
67607
7.7%
78021
8.1%
88266
8.3%
97939
8.0%
108476
8.6%
ValueCountFrequency (%)
127893
8.0%
117205
7.3%
108476
8.6%
97939
8.0%
88266
8.3%
78021
8.1%
67607
7.7%
58271
8.4%
47810
7.9%
38110
8.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size773.6 KiB
male
58749 
female
40252 

Length

Max length6
Median length4
Mean length4.813163503
Min length4

Characters and Unicode

Total characters476508
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male58749
59.3%
female40252
40.7%

Length

2022-10-30T18:10:58.011481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-30T18:10:58.097014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
male58749
59.3%
female40252
40.7%

Most occurring characters

ValueCountFrequency (%)
e139253
29.2%
m99001
20.8%
a99001
20.8%
l99001
20.8%
f40252
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter476508
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e139253
29.2%
m99001
20.8%
a99001
20.8%
l99001
20.8%
f40252
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Latin476508
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e139253
29.2%
m99001
20.8%
a99001
20.8%
l99001
20.8%
f40252
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII476508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e139253
29.2%
m99001
20.8%
a99001
20.8%
l99001
20.8%
f40252
 
8.4%

tenure
Real number (ℝ≥0)

Distinct2426
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537.8873749
Minimum0
Maximum3139
Zeros70
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:58.172576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q1226
median412
Q3675
95-th percentile1575
Maximum3139
Range3139
Interquartile range (IQR)449

Descriptive statistics

Standard deviation457.6498739
Coefficient of variation (CV)0.8508284359
Kurtosis2.199058275
Mean537.8873749
Median Absolute Deviation (MAD)213
Skewness1.535680925
Sum53251388
Variance209443.4071
MonotonicityNot monotonic
2022-10-30T18:10:58.256256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300173
 
0.2%
303170
 
0.2%
242164
 
0.2%
272163
 
0.2%
257161
 
0.2%
297161
 
0.2%
280160
 
0.2%
285160
 
0.2%
284158
 
0.2%
278158
 
0.2%
Other values (2416)97373
98.4%
ValueCountFrequency (%)
070
0.1%
160
0.1%
272
0.1%
379
0.1%
486
0.1%
592
0.1%
693
0.1%
784
0.1%
887
0.1%
993
0.1%
ValueCountFrequency (%)
31393
< 0.1%
31291
 
< 0.1%
31281
 
< 0.1%
31011
 
< 0.1%
30191
 
< 0.1%
29581
 
< 0.1%
29261
 
< 0.1%
28881
 
< 0.1%
28221
 
< 0.1%
27881
 
< 0.1%

friend_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2562
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.3528853
Minimum0
Maximum4923
Zeros1962
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:58.346044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q131
median82
Q3206
95-th percentile720
Maximum4923
Range4923
Interquartile range (IQR)175

Descriptive statistics

Standard deviation387.3078082
Coefficient of variation (CV)1.972508871
Kurtosis50.0932514
Mean196.3528853
Median Absolute Deviation (MAD)64
Skewness6.058947263
Sum19439132
Variance150007.3383
MonotonicityNot monotonic
2022-10-30T18:10:58.437962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01962
 
2.0%
11816
 
1.8%
21117
 
1.1%
3860
 
0.9%
5789
 
0.8%
4749
 
0.8%
10737
 
0.7%
24732
 
0.7%
6720
 
0.7%
29719
 
0.7%
Other values (2552)88800
89.7%
ValueCountFrequency (%)
01962
2.0%
11816
1.8%
21117
1.1%
3860
0.9%
4749
 
0.8%
5789
0.8%
6720
 
0.7%
7671
 
0.7%
8718
 
0.7%
9700
 
0.7%
ValueCountFrequency (%)
49231
< 0.1%
49171
< 0.1%
48631
< 0.1%
48451
< 0.1%
48441
< 0.1%
48261
< 0.1%
48171
< 0.1%
48031
< 0.1%
47971
< 0.1%
47941
< 0.1%

friendships_initiated
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1519
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.4537025
Minimum0
Maximum4144
Zeros2997
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:58.532479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median46
Q3117
95-th percentile418
Maximum4144
Range4144
Interquartile range (IQR)100

Descriptive statistics

Standard deviation188.7886575
Coefficient of variation (CV)1.756930224
Kurtosis42.53473985
Mean107.4537025
Median Absolute Deviation (MAD)36
Skewness5.150702922
Sum10638024
Variance35641.15721
MonotonicityNot monotonic
2022-10-30T18:10:58.912350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02997
 
3.0%
12212
 
2.2%
21551
 
1.6%
31355
 
1.4%
41352
 
1.4%
51328
 
1.3%
61328
 
1.3%
111319
 
1.3%
81314
 
1.3%
131279
 
1.3%
Other values (1509)82966
83.8%
ValueCountFrequency (%)
02997
3.0%
12212
2.2%
21551
1.6%
31355
1.4%
41352
1.4%
51328
1.3%
61328
1.3%
71237
1.2%
81314
1.3%
91245
1.3%
ValueCountFrequency (%)
41441
< 0.1%
36541
< 0.1%
35941
< 0.1%
35381
< 0.1%
34151
< 0.1%
32381
< 0.1%
32331
< 0.1%
30861
< 0.1%
30781
< 0.1%
30241
< 0.1%

likes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2924
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.0806457
Minimum0
Maximum25111
Zeros22308
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:59.029081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q381
95-th percentile726
Maximum25111
Range25111
Interquartile range (IQR)80

Descriptive statistics

Standard deviation572.2862744
Coefficient of variation (CV)3.666606273
Kurtosis200.4417006
Mean156.0806457
Median Absolute Deviation (MAD)11
Skewness11.02359342
Sum15452140
Variance327511.5799
MonotonicityNot monotonic
2022-10-30T18:10:59.127722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022308
22.5%
16928
 
7.0%
24434
 
4.5%
33240
 
3.3%
42507
 
2.5%
52027
 
2.0%
61806
 
1.8%
71618
 
1.6%
81430
 
1.4%
91381
 
1.4%
Other values (2914)51322
51.8%
ValueCountFrequency (%)
022308
22.5%
16928
 
7.0%
24434
 
4.5%
33240
 
3.3%
42507
 
2.5%
52027
 
2.0%
61806
 
1.8%
71618
 
1.6%
81430
 
1.4%
91381
 
1.4%
ValueCountFrequency (%)
251111
< 0.1%
216521
< 0.1%
167321
< 0.1%
165831
< 0.1%
147991
< 0.1%
143551
< 0.1%
140501
< 0.1%
140391
< 0.1%
136921
< 0.1%
136221
< 0.1%

likes_received
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2681
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.6914476
Minimum0
Maximum261197
Zeros24428
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:59.216279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q359
95-th percentile561
Maximum261197
Range261197
Interquartile range (IQR)58

Descriptive statistics

Standard deviation1387.933546
Coefficient of variation (CV)9.726816634
Kurtosis17384.5924
Mean142.6914476
Median Absolute Deviation (MAD)8
Skewness112.0734525
Sum14126596
Variance1926359.527
MonotonicityNot monotonic
2022-10-30T18:10:59.323825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024428
24.7%
17305
 
7.4%
24541
 
4.6%
33347
 
3.4%
42669
 
2.7%
52372
 
2.4%
61873
 
1.9%
71680
 
1.7%
81538
 
1.6%
91351
 
1.4%
Other values (2671)47897
48.4%
ValueCountFrequency (%)
024428
24.7%
17305
 
7.4%
24541
 
4.6%
33347
 
3.4%
42669
 
2.7%
52372
 
2.4%
61873
 
1.9%
71680
 
1.7%
81538
 
1.6%
91351
 
1.4%
ValueCountFrequency (%)
2611971
< 0.1%
1781661
< 0.1%
1520141
< 0.1%
1060251
< 0.1%
826231
< 0.1%
535341
< 0.1%
529641
< 0.1%
456331
< 0.1%
424491
< 0.1%
395361
< 0.1%

mobile_likes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2396
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.1182715
Minimum0
Maximum25111
Zeros35055
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:59.419362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q346
95-th percentile482
Maximum25111
Range25111
Interquartile range (IQR)46

Descriptive statistics

Standard deviation445.2572647
Coefficient of variation (CV)4.195858624
Kurtosis360.9816892
Mean106.1182715
Median Absolute Deviation (MAD)4
Skewness14.16110124
Sum10505815
Variance198254.0318
MonotonicityNot monotonic
2022-10-30T18:10:59.508886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035055
35.4%
16297
 
6.4%
23941
 
4.0%
32917
 
2.9%
42265
 
2.3%
51794
 
1.8%
61598
 
1.6%
71395
 
1.4%
81212
 
1.2%
91149
 
1.2%
Other values (2386)41378
41.8%
ValueCountFrequency (%)
035055
35.4%
16297
 
6.4%
23941
 
4.0%
32917
 
2.9%
42265
 
2.3%
51794
 
1.8%
61598
 
1.6%
71395
 
1.4%
81212
 
1.2%
91149
 
1.2%
ValueCountFrequency (%)
251111
< 0.1%
216521
< 0.1%
167321
< 0.1%
140391
< 0.1%
135291
< 0.1%
129341
< 0.1%
126391
< 0.1%
121041
< 0.1%
120831
< 0.1%
119591
< 0.1%

mobile_likes_received
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2004
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.12194826
Minimum0
Maximum138561
Zeros30003
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:59.599416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q333
95-th percentile317
Maximum138561
Range138561
Interquartile range (IQR)33

Descriptive statistics

Standard deviation839.8978639
Coefficient of variation (CV)9.984289252
Kurtosis15522.33964
Mean84.12194826
Median Absolute Deviation (MAD)4
Skewness107.530233
Sum8328157
Variance705428.4218
MonotonicityNot monotonic
2022-10-30T18:10:59.684961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030003
30.3%
18243
 
8.3%
24948
 
5.0%
33607
 
3.6%
42944
 
3.0%
52383
 
2.4%
62022
 
2.0%
71745
 
1.8%
81521
 
1.5%
91437
 
1.5%
Other values (1994)40148
40.6%
ValueCountFrequency (%)
030003
30.3%
18243
 
8.3%
24948
 
5.0%
33607
 
3.6%
42944
 
3.0%
52383
 
2.4%
62022
 
2.0%
71745
 
1.8%
81521
 
1.5%
91437
 
1.5%
ValueCountFrequency (%)
1385611
< 0.1%
1312441
< 0.1%
899111
< 0.1%
733331
< 0.1%
434101
< 0.1%
307541
< 0.1%
303871
< 0.1%
273531
< 0.1%
207701
< 0.1%
189251
< 0.1%

www_likes
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1726
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.96231351
Minimum0
Maximum14865
Zeros60999
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:59.783710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile208
Maximum14865
Range14865
Interquartile range (IQR)7

Descriptive statistics

Standard deviation285.5629301
Coefficient of variation (CV)5.715566596
Kurtosis449.1400484
Mean49.96231351
Median Absolute Deviation (MAD)0
Skewness16.91087447
Sum4946319
Variance81546.18707
MonotonicityNot monotonic
2022-10-30T18:10:59.872505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060999
61.6%
14696
 
4.7%
22760
 
2.8%
31948
 
2.0%
41419
 
1.4%
51202
 
1.2%
61081
 
1.1%
7897
 
0.9%
8792
 
0.8%
9757
 
0.8%
Other values (1716)22450
 
22.7%
ValueCountFrequency (%)
060999
61.6%
14696
 
4.7%
22760
 
2.8%
31948
 
2.0%
41419
 
1.4%
51202
 
1.2%
61081
 
1.1%
7897
 
0.9%
8792
 
0.8%
9757
 
0.8%
ValueCountFrequency (%)
148651
< 0.1%
129031
< 0.1%
110771
< 0.1%
107631
< 0.1%
106271
< 0.1%
105391
< 0.1%
102551
< 0.1%
102321
< 0.1%
99021
< 0.1%
94311
< 0.1%

www_likes_received
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1636
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.56945889
Minimum0
Maximum129953
Zeros36864
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2022-10-30T18:10:59.965258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile227
Maximum129953
Range129953
Interquartile range (IQR)20

Descriptive statistics

Standard deviation601.4223958
Coefficient of variation (CV)10.26853256
Kurtosis23811.77179
Mean58.56945889
Median Absolute Deviation (MAD)2
Skewness126.2560557
Sum5798435
Variance361708.8982
MonotonicityNot monotonic
2022-10-30T18:11:00.085886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036864
37.2%
18513
 
8.6%
25110
 
5.2%
33586
 
3.6%
42828
 
2.9%
52317
 
2.3%
61918
 
1.9%
71602
 
1.6%
81445
 
1.5%
91373
 
1.4%
Other values (1626)33445
33.8%
ValueCountFrequency (%)
036864
37.2%
18513
 
8.6%
25110
 
5.2%
33586
 
3.6%
42828
 
2.9%
52317
 
2.3%
61918
 
1.9%
71602
 
1.6%
81445
 
1.5%
91373
 
1.4%
ValueCountFrequency (%)
1299531
< 0.1%
621031
< 0.1%
396051
< 0.1%
392131
< 0.1%
340391
< 0.1%
326921
< 0.1%
293371
< 0.1%
231471
< 0.1%
226441
< 0.1%
150961
< 0.1%

Interactions

2022-10-30T18:10:54.498985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:30.320224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.992514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.762031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.523103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.203127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.001115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.653337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.337581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:44.042682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:45.905406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.533192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.163455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.808550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.774243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:54.623653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:30.450861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:32.116545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.869089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.631934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.315767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.107637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.765860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.438300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:44.151274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.011950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.649710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.265943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.921101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.885860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:54.746736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:30.558665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:32.243085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.977605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.738725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.426071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.223855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.873375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.546730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:44.259709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.115483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.764401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.378461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:51.034611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.988573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:54.855289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:30.657171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:32.350311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:34.221303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.844124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.528900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.325383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.978894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.649346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:44.366830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.218018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.868867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.505413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:51.140647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:53.095005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:54.982808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:30.768701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:32.466096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:34.343326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.962453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.643869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.426274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:41.096041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.762963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:44.679437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.324538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.976614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.613978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:51.278559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:53.217115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.096363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:30.883394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:32.582705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:34.446799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.081989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.754588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.533015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:41.209082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.870752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:44.779226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.431144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.086381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.726955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:51.399523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:53.336309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.210940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:30.995915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:32.688397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:34.544415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.197501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.860106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.638469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:41.322415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.982356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:44.883754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.535750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.190162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.840006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:51.747692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:53.455770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.331464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.110440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:32.811917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:34.645046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.309021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.972672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.748585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:41.452934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:43.091798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:44.993597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.645788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.292942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.959618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:51.859807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:53.593580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.444519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.223547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:32.927978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:34.751553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.426171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:38.078391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.866758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:41.556652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:43.202671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:45.102360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.741308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.399406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.077150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:51.977358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:53.725348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.551064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.338708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.037542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:34.858268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.539708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:38.183621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:39.980326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:41.668047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:43.305449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:45.201884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.858693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.501913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.184657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.088338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:53.831876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.648687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.447521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.149694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:34.959400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.641809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:38.288285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.087186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:41.783841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:43.405237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:45.316581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:46.962211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.607443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.288600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.207884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:53.947915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.756732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.553040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.269380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.065912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.755343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:38.393001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.196112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:41.888372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:43.513781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:45.425194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.067726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.712999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.399155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.322518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:54.058520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.865279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.659452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.392896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.175980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.865396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:38.493556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.321140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.001922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:43.628039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:45.533240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.168255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.813615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.494724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.434091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:54.160077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:55.979806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.772902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.512532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.286562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:36.987920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:38.606301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.431426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.119616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:43.772855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:45.661307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.273903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:48.923459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.598326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.547141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:54.267648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:56.103416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:31.884512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:33.638520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:35.409080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:37.096062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:38.874090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:40.538157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:42.231680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:43.906120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:45.791858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:47.409077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:49.039014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:50.704854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:52.665712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-10-30T18:10:54.382460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-10-30T18:11:00.186423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-10-30T18:11:00.335952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-30T18:11:00.479004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-30T18:11:00.622534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-30T18:11:00.770961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-30T18:10:56.267531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-30T18:10:56.547153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexuseridagedob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
0020943821419199911male266.000000000
111192601142199911female6.000000000
2220838841416199911male13.000000000
3312031681425199912female93.000000000
441733186144199912male82.000000000
551524765141199912male15.000000000
661136133131420001male12.000000000
77168036113420001female0.000000000
88136517413120001male81.000000000
99171256713220002male171.000000000

Last rows

df_indexuseridagedob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
98991989931654565191519948male394.04538414445011508844355961669127
9899298994206300620419931female402.01988332735110602572487333310332692
98993989951132164209199310female699.03611973450777684414690993859
98994989961668695242519894female182.0293812726018177655843117081756057
989959899714589852814198512female290.022181618462610268429042503366018
9899698998126829968419454female541.021183413996180893505118874916202
98997989991256153181219953female21.01968172044011341243991059222820
98998990001195943151019985female111.0200215241195912554119591146201092
98999990011468023231119904female416.0256018545066516450657600756
99000990021397896391519745female397.020497689410124439410953002913